Lumbar Pain Icd 10


Lumbar Pain Icd 10
ICD-10 Code: M54.5 – Low Back Pain – ICD-Code M54.5 is a billable ICD-10 code used for healthcare diagnosis reimbursement of chronic low back pain. Its corresponding ICD-9 code is 724.2. Billable: Yes ICD-9 Code Transition: 724.2 Code M54.5 is the diagnosis code used for Low Back Pain (LBP). This is sometimes referred to as lumbago. Other Synonyms Include:

Acute low back painC/O – loin painC/O – low back painChronic low back painComplaining of backacheFacet joint painFinding of sensation of lumbar spineIntractable low back painLoin painLow back painLow back pain in pregnancyLumbar ache – renalLumbar facet joint painLumbar spine – tenderLumbar spine painful on movementLumbar trigger point syndromeMechanical low back painMyofascial painMyofascial pain syndromeO/E – lumbar pain on palpationOn examination – abdominal pain on palpationPain in lumbar spinePain radiating to lumbar region of backPosterior compartment low back painPostural low back painSacral back painSacrocoxalgiaTenderness of left lumbarTenderness of right lumbar

What is the ICD-10 code for lumbar back pain unspecified?

ICD-10-CM Code for Low back pain, unspecified M54.50.

What can I use instead of M54 5?

Providers will need to get specific, using more detailed (and sometimes new) ICD-10 codes to describe low back pain. – So, here’s the real question: How the heck can CMS justify deleting such a commonly used code? Well, CMS has explained that it’s deleting M54.5 because it lacks specificity (and we all know how important is to ICD-10).

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S39.012, Low back strainM51.2-, Lumbago due to intervertebral disc displacementM54.4-, Lumbago with sciaticaM54.50, Low back pain, unspecifiedM54.51: Vertebrogenic low back painM54.59: Other low back pain

Please note that using, M51.2-, or M54.4- in addition to M54.5- will likely result in,

What is the ICD-10 code for lower back injury?

ICD-10 code S39.92XA for Unspecified injury of lower back, initial encounter is a medical classification as listed by WHO under the range – Injury, poisoning and certain other consequences of external causes.

What is the ICD 9 code for low back pain unspecified?

ICD-9 Code 724.5 -Backache unspecified- Codify by AAPC.

What is the ICD 9 code for lumbar spine pain?

Chronic lumbosacral sprain/strain: ICD-9-CM Code 724.

What is the ICD-10 for episodic low back pain?

Table 1 – The classification results in identifying clinical notes with acute low back pain (LBP) episodes averaged over the 10-fold cross-validation experiment. We compared different supervised and unsupervised strategies: keyword search (WordSearch), topic modeling (TopicModel), logistic regression with bag of n-grams (BoN-LR) and manual features (FeatEng-LR), and deep learning (ConvNet).

Model Precision Recall F score Area under the receiver operating characteristic curve Area under the precision-recall curve
ICD-10 a 0.32 0.68 0.41 0.81 0.42
Unsupervised methods
WordSearch 0.71 0.03 0.06 0.52 0.40
TopicModel 0.44 0.58 0.50 0.92 0.46
Trained with the M54.5 ICD-10 code
BoN-LR b 0.50 0.70 0.59 0.83 0.42
FeatEng-LR c 0.47 0.59 0.52 0.88 0.41
ConvNet d 0.55 0.68 0.61 0.89 0.46
Trained with manual annotations
BoN-LR 0.53 0.64 0.58 0.93 0.56
FeatEng-LR 0.58 0.66 0.62 0.93 0.58
ConvNet 0.65 0.73 0.70 0.98 0.72

Receiver operating characteristic and precision-recall curves obtained when using as training data for BoN-LR, FeatEng-LR and ConvNet the manual annotations (a) and the M54.5 ICD-10 codes (b). ConvNet trained using the manual annotations obtained the best results.

  • In the absence of manual annotations to use for training, TopicModel worked better than methods trained using ICD-10 codes, which proved not to be a good indicator to identify acuity in low back pain episodes.
  • BoN-LR: logistic regression with bag of n-grams; ConvNet: convolutional neural network-based architecture; FeatEng-LR: logistic regression with feature engineering; ICD-10: international classification of diseases, 10th revision; PR: precision-recall; ROC: receiver operating characteristic.

Figure 3 shows the classification results in terms of AUC-ROC and AUC-PRC when randomly subsampling the acute LBP manual annotations in the training set. We found that ConvNet always outperforms the other methods based on LR as well as TopicModel. In addition, we notice that using just 240 out of 800 (30.0%) manual annotations in the training set already leads to better results than using ICD-10 codes as training labels. Area under the receiver operating characteristic and precision-recall curves obtained when training the supervised models using random subsamples of the manual annotations. TopicModel is reported as reference baseline. ConvNet obtained satisfactory results when trained using less manually annotated documents, showing robustness and scalability to the gold standard.

AUC-PRC: area under the precision-recall curve; AUC-ROC: area under the receiver operating characteristic curve; BoN-LR: logistic regression with bag of n-grams; ConvNet: convolutional neural network-based architecture; FeatEng-LR: logistic regression with feature engineering. Figure 4 highlights the distributions of the classification scores (predicted probability of the label acute LBP ) derived by several supervised models (trained with manual annotations) and TopicModel.

ConvNet shows a clear separation between acute LBP notes and the rest of the dataset. In particular, all acute LBP notes had scores greater than 0.2, with 81.6% (727/891) of them having scores greater than 0.5. On the contrary, only 347 controls had scores greater than 0.5, meaning that only a few notes were highly likely to be misclassified. Representation of the probability distribution of the scores obtained by BoN-LR, FeatEng-LR, ConvNet, and TopicModel. ConvNet led to a good separation between acute low back pain clinical notes and all the other documents. In other cases, such separation is not as clear, explaining the worse classification results obtained by those models.

BoN-LR: logistic regression with bag of n-grams; ConvNet: convolutional neural network-based architecture; FeatEng-LR: logistic regression with feature engineering. Finally, Table 2 summarizes some of the n-grams driving the acute LBP predictions obtained by ConvNet (trained with manual annotations) across the experiments.

Although some of these are obvious and refer to the disease itself (eg, “acute lbp”), others refer to medications (eg, “prescribed muscle relaxant” and “flexeril”) and recommendations (eg, “rtw full duty quick”). Given their clinical meaning and relevance, all these patterns can be further analyzed and reviewed to potentially drive the development of guidelines for, for example, treatment and RTW options.